float-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It states it's a list operation with filtering, implying read-only behavior, but doesn't disclose pagination details (beyond parameters), rate limits, authentication needs, or what 'allocations' entail (e.g., tasks in Float API context). This leaves significant behavioral gaps for a tool with 7 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that states the core purpose without fluff. However, it could be more front-loaded by explicitly mentioning it's for retrieving allocation data, and the Float API note, while concise, doesn't add immediate clarity for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 7 parameters with full schema coverage, the description is minimally adequate. It covers the basic purpose but lacks behavioral context (e.g., pagination handling, error cases) and doesn't explain return values, leaving gaps for a list tool with filtering options.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds minimal value by mentioning 'optional filtering' and the Float API equivalence, but doesn't provide additional context like default behaviors or parameter interactions. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('allocations') with optional filtering, making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'list-projects' or 'list-people' beyond mentioning the Float API equivalence, which is somewhat helpful but not a direct sibling comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as 'get-allocation' for a single allocation or other list tools for different resources. It mentions the Float API equivalence, but this is not actionable usage advice for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.